Driver fatigue state detection based on EEG and EOG feature fusion and deep learning

Fatigue driving is an important factor leading to traffic accidents, often causing serious consequences. Therefore, real-time detection of driver fatigue is the key to avoid fatigue driving. Aiming at the single detection standard that can easily reduce the accuracy of driver fatigue detection, a neural network method based on the multi-modal fusion of forehead EEG and ocular signals is proposed to fully mine the complementary information of the two signal characteristics, and using SEEDVIG, the public data set of Shanghai Jiao tong University for training. The experimental results show that compared with a single modal. Multi-modal fusion has a better recognition effect for fatigue detection, and its accuracy rate reaches 96.43%, which is helpful to promote the application of the fatigue detection system based on EEG signals in the driving process of the driver.